A python implementation of the protein frustratometer.
XKCD comic showing a clasic example of frustration at the movie theather
The frustratometer is based on the principle of minimal frustration postulated by Wolynes et al. Proteins have evolved under a selective pressure to fold into a single native structure, which is energetically minimized by the evolutionary process, but also to not fold into the many possible incorrect glassy conformations, where the energy is maximized by the evolutionary process. In that principle it is expected that when we measure the energy of a correctly folded protein, most of the interactions between the amino acids will be minimized, compared to both a different amino acid in the same position (mutational) and the same amino acid in a different position (configurational). When this energy is minimized for a particular residue or interaction we define it to be minimally frustrated.
Under most circunstances amino acids are minimally frustrated in the protein, but pockets where amino acids are of interest for protein function can remain frustrated, as they become minimally frustrated when achieving the corresponding function. In short, a frustrated residue or interaction indicates that the particular amino acid would be minimized under a different environment, which reveals possible competing evolutionary pressures on its selection. We have identified that these pockets usually correspond to regions of functional importance, for example, that may form part of a catalitic domain, a hinge domain, or a binding region.
This principle of minimum frustration has been shown using the AWSEM forcefield but can be extended to any other forcefield, including atomistic forcefields, or a pseudo-forcefield like DCA, as implemented here.
To install this modules please clone this repository and install it using the following commands.
git clone HanaJaafari/Frustratometer
cd Frustratometer
conda install -c conda-forge --file requirements.txt
pip install -e .
There are additional packages that can be installed and are detailed in the documentation.
The Frustatometer package includes a prody based Structure class to load the structure and calculate the properties needed for the AWSEM and DCA Frustratometers.
import frustratometer
# Define the path to your PDB file
pdb_path = Path('data/my_protein.pdb')
# Load the structure
structure = frustratometer.Structure.full_pdb(pdb_path)
structure.sequence #The sequence of the structureAfter loading the structure, create an AWSEM model instance with the desired parameters. Here we provide some typical configurations that can be found elsewhere.
## Single residue frustration with electrostatics
model_singleresidue = frustratometer.AWSEM(structure, min_sequence_separation_contact=2)
## Single residue frustration without electrostatics
model_singleresidue_noelectrostatics = frustratometer.AWSEM(structure, min_sequence_separation_contact=2, k_electrostatics=0)
## Mutational/Configurational frustration with electrostatics
model_mutational = frustratometer.AWSEM(structure)
## Mutational/Configurational frustration without electrostatics
model_mutational_noelectrostatics = frustratometer.AWSEM(structure, k_electrostatics=0)
## Mutational frustration with sequence separation of 12
model_mutational_seqsep12 = frustratometer.AWSEM(structure, min_sequence_separation_rho=13)
## Typical openAWSEM
model_openAWSEM = frustratometer.AWSEM(structure, min_sequence_separation_contact = 10, distance_cutoff_contact = None)After loading the structure, create a DCA model instance with the desired parameters. When creating a DCA model instance, you can provide an existing Potts model or a Potts model file (in Matlab format). Additionally, the Frustratometer can generate a Potts model for a given protein via the pydca package.
potts_model_file=Path('data/my_potts_model.mat')
## Using existing potts model file
model_dca = frustratometer.DCA.from_potts_model_file(structure,potts_model_file,distance_cutoff=4,sequence_cutoff=0)In order to check the accuracy of the contacts predicted by the Potts model and true contacts from the protein's contact map, the receiver operator curve (ROC) can be generated. If the Potts model has high accuracy, the Area Under the Curve (AUC) of the ROC will be 1.
## Generate the ROC curve
model_dca.plot_roc()
##Calculate AUC of ROC curve
print(model_dca.auc())To calculate the density of residues in the structure.
calculated_densities = model_singleresidue.rho_r
print(calculated_densities)Frustration indices can be calculated for single residues or mutationally. This measurement helps identify energetically favorable or unfavorable interactions within the protein structure.
# Calculate single residue AWSEM frustration
single_residue_AWSEM_frustration = model_singleresidue.frustration(kind='singleresidue')
print(single_residue_AWSEM_frustration)
# Calculate single residue DCA frustration
single_residue_DCA_frustration = dca_model.frustration(kind='singleresidue')
print(single_residue_DCA_frustration)The frustratometer package also allows the quick calculation of the energies of all single residue and mutational decoys.
# Calculate single residue decoy AWSEM energy fluctuations
AWSEM_decoy_fluctuation = model_singleresidue.decoy_fluctuation(kind='singleresidue')
print(AWSEM_decoy_fluctuation)
# Calculate single residue decoy DCA energy fluctuations
DCA_decoy_fluctuation = dca_model.decoy_fluctuation(kind='singleresidue')
print(DCA_decoy_fluctuation)# Calculate mutational AWSEM frustration
mutational_AWSEM_frustration = model_mutational.frustration(kind='mutational')
print(mutational_AWSEM_frustration)
# Calculate mutational DCA frustration
mutational_DCA_frustration = dca_model.frustration(kind='mutational')
print(mutational_DCA_frustration)You can calculate different energy contributions, including fields energy (pseudo one-body terms like burial), couplings energy (pseudo two-body terms like contact and electrostatics), and their combination to determine the native energy of the protein structure.
AWSEM_fields_energy = model_openAWSEM.fields_energy()
print(AWSEM_fields_energy)
DCA_fields_energy = dca_model.fields_energy()
print(DCA_fields_energy)AWSEM_couplings_energy = model_openAWSEM.couplings_energy()
print(AWSEM_couplings_energy)
DCA_couplings_energy = dca_model.couplings_energy()
print(DCA_couplings_energy)Native energy can be considered as a combination of fields and couplings energy contributions.
AWSEM_native_energy = model_openAWSEM.native_energy()
print(AWSEM_native_energy)
DCA_native_energy = dca_model.native_energy()
print(DCA_native_energy)The Frustratometer AWSEM package offers many functionalities for analyzing protein structures. By calculating residue densities, frustration indices, and various energy contributions, researchers can gain insights into the stability, energetics, and potentially functional aspects of protein conformations.
The frustratometer has been implemented in other ways by our group:
Copyright (c) 2022-2024, Carlos Bueno, Hana Jaafari
This work was supported by National Science Foundation (NSF) Center for Theoretical Biological Physics (NSF PHY-2019745) and NSF grants PHY-1522550. Project skeleton based on the [Computational Molecular Science Python Cookiecutter] (https://github.com/molssi/cookiecutter-cms) version 1.6.
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